Index modeling and application of multi-resolution logistics nodes location based on Granular Computing

Abstract

The paper presents a novel framework about Multi-Resolution Model (MRM) of Logistics Node Location(LNL) based on the theory of Granular Computing(GC), which integrates the macroscopic, mesoscopic and microscopic logistics location problem. Simultaneously, the paper also discusses the concept of Attribute Reduction(AR) and establishes the application framework of LNL problem. In addition, in logistics location, the minimum attribute reduction, which is a NP-HARD problem, is a core issue. In order to solve the problem, the paper establishes a minimal reduction accurate algorithm. Finally, it develops a Logistics node location system in VB and C++. Then, it simulates and analyses the results based on the location of freight services logistics park in Changsha. Simulation result shows that MRM has a high utility and convenience and the algorithm is effective.